In this project I performed an exploratory analysis on data provided by Ford GoBike, a bike-share system provider, using Python visualization techniques. The goal is to figure out what variables possess the most influential power on a bike sharing service. I did the analysis on 2019 year data
Columns:
Understand he number of subscribers/customers in the dataset and which of them are more valuable to the company.
The time for which the bike is rented on an average and the distance in miles travelled by different classes of users.
Understand whether the start time of rentals differ between subscribers and customers.
The locations which sees the most rentals among different classes of users.
From Normal Plot: Distribution of rental duration is right-skewed, there are rentals for about an hour or so by users.
From the Log Plot: Rental duration is roughly bimodal.
/home/abdulrahman/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation. warnings.warn(
Subscribers rented the bikes for less time than customers but travelled more they seems to be in hurry.
Relationship between start Day, start Hour and user type which give a hint about the nature of the user type.
Subscribers uses bikes as transportation option.
Subscribers are regular customers who are making rides to/from work or school, renting a bike at 7-9am and 4-6pm on weekdays
Customers rent bikes for exploring the Bay area and they could be tourists.
Customers could be tourists who use bikes to explore the Bay area mainly on weekends.